Network security defence system based on artificial intelligence and big data technology

Facing the problem of power system data loss, this paper proposes a power system data recovery method based on a generative adversarial network. The power system clustering method utilizes aggregated hierarchical clustering and takes into consideration the similarity between different power system data. To transform the power system data recovery problem into a data generation problem, an improved GAN network data analysis method is proposed that utilizes LSTM as a generator and discriminator. Through experimental tests, the LSTM-GAN method is tested with the LSTM method, interpolation method and low-rank method to compare its effect on lost data recovery under different signals of power system data static and dynamic and four fault scenarios. The results show that the root-mean-square errors of the LSTM-GAN method for recovering data under static-dynamic fluctuations are less than 1.2%, and the difference between the errors under 55% and 15% missing data conditions is only 0.77%, with the highest data recovery error of 2.32% in the power system fault scenarios. Therefore, the GAN-based power system data recovery method can effectively realize the recovery of lost data.

Section: Introductionmentioning

confidence: 99%

“…Di Yang, Ming Ji, Yuntong Lv, Mengyu Li and Xuezhe Gao. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] …”

mentioning

confidence: 99%

Generative Adversarial Network-based Data Recovery Method for Power Systems

Yang,

Ji,

et al. 2024

“…Rawat, D. B. et al described the current status and development trend of big data technology applied to the prevention of network security problems, which effectively helps readers understand the importance of data security in the network era [14]. Xie, L. et al introduced artificial intelligence technology into the BDASS network security prevention system to upgrade and improve the system's efficiency and risk identification accuracy [15]. Jensen et al designed a network vulnerability detection system composed of big data technology and machine learning algorithms as the underlying architecture, introduced its detection principle, and carried out theoretical verification [16].…”

Section: Introductionmentioning

confidence: 99%

The Role of Mining and Detection of Big Data Processing Techniques in Cybersecurity

2024

The need for advanced detection methods has become more critical in light of the increasing prevalence of network security incidents. This study proposes a novel approach to network security detection using a fuzzy data mining algorithm, addressing the rising challenges in big data processing and network security. The paper outlines the evolution of big data analytics by exploring the integration of network security detection, data mining, and structural feature analysis. Data for this research was collected using a sniffer device and underwent extensive preprocessing to ensure diversity and applicability. To overcome the limitations of traditional data mining, such as the issue of sharp boundaries, this method combines fuzzy logic with data mining techniques, enhancing conventional network security protocols. Simulation experiments demonstrate the efficacy of this fuzzy mining-based approach, with results showing 987,238 predicted positive cases, 93,951 of which were accurate. The method achieves an impressive 93.65% accuracy and 92.55% recall rate, proving its capability to promptly identify and mitigate suspicious network activities.

“…Network security has become a hot topic worldwide due to the rapid development of network technology and the popularity of the Internet [1][2][3]. The forms and threats of network security attacks have become increasingly complex and serious, and traditional security defense technologies have been unable to meet the detection and response requirements for new types of threats.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Based Large Scale Network Security Threat Detection Techniques

2024

This paper constructs a detection technique for large-scale network security threats based on a knowledge graph, extracts the attack features of network security threats using feature template FT, and combines the CNN layer, BiLSTM layer and CRF layer to establish FT-CNN-BiLSTM-CRF large-scale network security threat detection technique. Network security threat performance evaluation experiments and multi-step attack experiments have verified the detection capability of this paper's method. The recall rate of the method built in this paper in detecting malicious data is about 62.39%, the average F1-Score for normal and malicious traffic detection is 0.7482, and the anomaly score for normal traffic detection is almost 0. The detection performance of this paper's method for multi-step network attacks is superior to that of other methods, and it is capable of detecting malicious attacks quickly. Experiments have proved that the method constructed in this paper can meet the requirements of detection capability and efficiency in large-scale network security threats and has high feasibility and application value.